System for dynamic inventory control

ABSTRACT

A production management system is configured to dynamically controlling inventory of a semiconductor product to prevent overstock and stockout. The production management system includes a production planning module including components containing data of demand forecast, and customer order. The production management system further includes a dynamic inventory control module including a dynamic inventory control simulation module and an inventory management system, wherein the dynamic inventory control simulation module is configured to adjust a target inventory if a current inventory is beyond a threshold multiplied by the target inventory for M number of review cycles.

PRIORITY CLAIM

The present application is a divisional of U.S. application Ser. No.13/728,703, filed Dec. 27, 2013, which is a divisional of U.S.application Ser. No. 12/697,510, filed Feb. 1, 2010, now U.S. Pat. No.8,364,512, both of which are incorporated by reference herein in theirentireties.

FIELD

This application relates to supply chain management and, moreparticularly, to inventory control.

BACKGROUND

The prevalent usage of Internet for information gathering and sharing,social networking, and commerce have given users reasons to demand newcomputation and communication hardware with more memories and fasterprocessing rates. The advancement of semiconductor manufacturing haslowered the cost of manufacturing semiconductor chips and makes such newhardware affordable to the majority of people all over the world.Examples of hardware that utilize such new semiconductor chips includecomputers, personal digital assistants (PDAs), mobile phones, globalpositioning systems (GPSs), etc. Further, with the lowering ofmanufacturing costs of semiconductor chips, the usage of semiconductorchips are increasing expanded in other fields and applications. Forexample, more and more components of automobiles and home appliancesinclude semiconductor chips as controllers, sensors, displays, etc. Withthe expanded applications, processing capabilities, and/or storagecapacities, the demand for semiconductor chips has greatly increased. Atthe same time, the chip design and product cycles of semiconductor chipshave both shortened to meet the demand of end users. Semiconductormanufacturers also need to respond to the shortened cycles to bringproducts to the market in a timely manner.

The manufacturing of semiconductor chips involves substrate processingto make devices and die packaging. Semiconductor substrate processinginvolves film deposition, lithographical patterning, dopant implant,etching, planarization, cleaning, etc. Some semiconductor chips require50 lithographical layers or more to define and to connect devices.Therefore, the manufacturing of semiconductor chips from bare siliconsubstrates to assembled and tested chips can take 2-3 months. Withinstability in the global economy and consumers' interests, managing theinventory of semiconductor chips is very challenging. Overstocking ofsemiconductor chips very costly. On the other hand, shortage of stock(or stockout) can pose serious problems for customers. How to properlymanage (or control) inventory to meet the demand is critical.

SUMMARY

Broadly speaking, the embodiments of the present application fill theneed of properly controlling product inventory of semiconductor chips byproviding methods and systems of dynamic inventory control. The methodsand systems timely modify parameters affecting inventory. The parametersmay include target inventory, cycle time, wafer start, future inventoryand future shipment. In addition, the methods and systems gatherreal-time customer demand forecast to assist in production planning andadjustment. Further, the methods and systems identify inventory controlturning points dynamically to adjust production activities to preventoverstock and to prevent stockout, i.e., out of stock situations.

In one embodiment, a method of controlling inventory of a product toprevent overstock is provided. The method includes an operation ofestablishing initial forecast of target inventory, wafer start,inventory, shipment, cycle time, upper inventory threshold, and lowerinventory threshold for a product. The method further includes anoperation of reviewing and comparing real inventory and target inventorydata on a periodic basis. The method also includes the operation ofdetermining if real inventory exceeds the upper inventory threshold fora number of consecutive review periods. If the answer is yes, the methodincludes reducing a forecast for target inventory, and proceeding to theoperation of determining if an end of product life cycle has beenreached. If the answer is no, proceeding to an operation of determiningif the end of product life cycle has been reached. In addition, themethod includes an operation of determining if an end of a product lifecycle has been reached. If the end of the product life cycle has beenreached, the operation is terminated. If the end of the product lifecycle has not been reached, the method returns to the operation ofreviewing and comparing real inventory and target inventory data.

In another embodiment, a method of controlling inventory of a product toprevent stockout is provided. The method includes an operation ofestablishing initial forecast of target inventory, wafer start,inventory, shipment, cycle time, upper inventory threshold, and lowerinventory threshold for a product. The method also includes theoperation of reviewing and comparing real inventory and target inventorydata on a periodic basis. The method further includes determining ifreal inventory is lower than the upper inventory threshold for a numberof consecutive review periods. If the answer is yes, the methoddecreases cycle time and increases wafer start, and proceeds to anoperation of determining if an end of a product life cycle has beenreached. If the answer is no, the method proceeds to an operation ofdetermining if the end of product life cycle has been reached. Inaddition, the method includes an operation of determining if the end ofthe product life cycle has been reached. If the end of the product lifecycle has been reached, the operation is terminated. If the end ofproduct life cycle has not been reached, the method returns to theoperation of reviewing and comparing real inventory and target inventorydata.

In yet another embodiment, a production management system to dynamicallycontrol inventory of a semiconductor product to prevent overstock andstockout is provided. The production management system includes aproduction planning module including components containing data ofdemand forecasts, and customer orders. The production management systemalso includes a dynamic inventory control module including a dynamicinventory control simulation module and an inventory management system.The inventory management system records real inventory data. The dynamicinventory control simulation module includes simulators for targetinventory, future inventory, future shipment, and semiconductor productproduction.

Other aspects and advantages of this disclosure will become apparentfrom the following detailed description, taken in conjunction with theaccompanying drawings, illustrating by way of example the principlesdisclosed by this application.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will be readily understood by the followingdetailed description in conjunction with the accompanying drawings, andlike reference numerals designate like structural elements.

FIG. 1 is a high-level flow diagram of an exemplary process flow 100 oforder and manufacturing of semiconductor chips.

FIG. 2 is a high-level functional block diagram of an information systemof a chip provider, and an information system of a chip manufacturer, inaccordance with one embodiment of this disclosure.

FIG. 3 is a high-level functional block diagram of a productionmanagement system, in accordance with one embodiment of this disclosure.

FIG. 4 is a graph of inventory and target inventory as a function oftime, in accordance with one embodiment of this disclosure.

FIG. 5A is a graph of inventory and shipment of a product with andwithout using the algorithm described above in Example I to correct thetarget inventory, in accordance with one embodiment of this disclosure.

FIG. 5B is a high-level process flow diagram of controlling productinventory, in accordance with one embodiment of this disclosure.

FIG. 6A is a graph of an inventory of a product with and without theutilization of the algorithm II described above to correct the targetinventory, in accordance with one embodiment of this disclosure.

FIG. 6B is a high-level process flow diagram of managing productinventory using algorithm II, in accordance with one embodiment of thisdisclosure.

FIG. 7A is a graph of an inventory of a product with and without theutilization of the algorithm III described above to correct the targetinventory, in accordance with one embodiment of this disclosure.

FIG. 7B is a high-level process diagram flow of managing productinventory using algorithm III, in accordance with one embodiment of thisdisclosure.

FIG. 8 is a high-level process flow diagram of managing productinventory, in accordance with one embodiment of this disclosure.

FIG. 9 is an example of a special-purpose or general-purpose computerfor implementing a dynamic inventory control system in accordance withat least one embodiment of this disclosure.

DETAILED DESCRIPTION

It is to be understood that the following disclosure provides manydifferent embodiments, or examples, for implementing different featuresdescribed in this disclosure. Specific examples of components andarrangements are described below to simplify the present disclosure.These are, of course, merely examples and are not intended to belimiting. In addition, the present disclosure may repeat referencenumerals and/or letters in the various examples. This repetition is forthe purpose of simplicity and clarity and does not in itself dictate arelationship between the various embodiments and/or configurationsdiscussed.

FIG. 1 shows an exemplary process flow 100 of order and manufacturing ofsemiconductor chips. The process starts with hardware sellers 101, suchas computer or cell phone manufacturers/sellers, ordering semiconductorchips, such as graphic chips, from a chip provider 102. In the examplehere, the chip provider is a fabless design company that relies onsemiconductor foundry companies to make the graphic chips that the chipprovider designs. Alternatively, an integrated device manufacturer (IDM)can also rely on semiconductor foundry companies to producesemiconductor chips. IDMs are chip providers that also have chipmanufacturing capabilities.

After receiving orders of semiconductor chips from the hardware sellers,the chip provider then places chip orders with a chip manufacturer 103(a semiconductor foundry company in this example) to make semiconductorchips. Based on the order(s), the chip manufacturer processes substratesin a fabrication facility (or fab) 104 to make and test the devices assemiconductor chips. Chip manufacturing materials, such as substrates,chemicals, and processing equipment, need to ordered and prepared toallow the substrate processing to take place. As mentioned above, manyprocessing steps are involved in the making of the semiconductor deviceson the substrates (or wafer). For example, the number of processingsteps can be 300 steps or more, and the number of lithographical layers(or patterning steps) can be 50, or more. To complete the entireprocessing sequence in the fab can take a few months.

After substrate processing is completed, circuits on each die of thesubstrates are electrically tested to determine how many dies on thesubstrates are usable (working dies). The substrates can be temporarilystored in a die bank before being shipped to an assembly facility 105.In the assembly facility, the semiconductor dies on the substrates aresawed and working dies are packaged. After packaging, the packaged diesundergo final tests to ensure that packaged dies are still functional.The packaging and final test performed at the assembly facility can takea few weeks. Afterwards, the finished chips are placed in storage beforebeing shipped to the delivery location(s) specified by the chipprovider.

As mentioned above, in order to produce semiconductor chips, chipmanufacturing materials, such as substrates, chemicals, etc., and chipprocessing equipment need to be ordered and prepared to allow thesubstrate processing, packaging, and testing to occur. Over-supply ofthe chip manufacturing materials and over-capacities (orunder-utilization) of the equipment are very costly, since some of thematerials and the equipment can be very expensive. For example, manytypes semiconductor manufacturing equipment cost multiple millions ofdollars each. In addition, if the demand forecast is not correct, theinventory of the semiconductor chips can be too great or too small. Toomuch chip inventory is costly for the chip manufacturers. Too littlechip inventory runs the risk of stockout, which results in not beingable to meet demands of customers.

Managing the supply chain activities of semiconductor manufacturing inorder to take orders to transform materials (such as substrates,chemicals, etc.) with the help of resources (such as equipment, people,etc.) into finished products (semiconductor chips), and then deliveringthese products to the customers becomes very crucial in reducing thecost of semiconductor manufacturing. Inventory control in the supplychain management for manufacturing of semiconductor chips is especiallyimportant due to the market fluctuation and short development andproduct cycles of semiconductor chips.

FIG. 2 shows an information system 200 of a chip provider A, and aninformation system 250 of a chip manufacturer B, in accordance with oneembodiment of this disclosure. In this example, chip manufacturer Bproduces semiconductor chips for chip provider A; therefore, chipprovider A is a customer of chip manufacturer B. The information system200 of chip provider A manages demand forecast, order (or orderplacement), and inventory. In the example shown in FIG. 2, the demandinformation system 200 includes modules managing demand forecast 201,order placement 203, and inventory 205. The demand forecast module 201may contain information from upstream customers, historical sales data,and market research data. For example, if chip provider A has a brandnew product, chip provider A might need to rely on market research datato determine how many such chips will be needed in the market. Inanother example, if chip provider A has a newer version of a product,chip provider A could utilize the sales data of the older version of theproduct to forecast how many chips will be needed. The demand forecastmodule 201 may also include a sub-module 202 that handles demand changein response to sudden (or unexpected) changes in the market. The orderplacement module 203 contains order information. The inventory module205 contains inventory information of various types of semiconductorchips of the chip provider A.

The information system 250 of chip manufacturer B is used to manageorders, manufacturing planning, manufacturing, material planning, andinventory of chips manufactured by chip manufacturer B. In the examplein FIG. 2, system 250 has an order management module 251, amanufacturing planning system 253, a material management module 254, amanufacturing information module 255, and an inventory module 257. Theorder management module 251 records the amounts, the types, and thedelivery dates of chips ordered by customers. The order information isfed to the manufacturing planning system 253, which determines themanufacturing schedule based on a number of factors, including the fabcapacity. In at least some embodiments, the manufacturing capacity andschedule of chip manufacture B also influence the order taking. Forexample, sometimes chip manufacture B may need to turn down certainorders due to lack of manufacturing capacity.

The manufacturing planning system 253 is coupled to a materialmanagement system 254, which manages the order and the supply ofmaterials needed to produce the chips. The manufacturing schedule isthen communicated to the fab, which records its manufacturinginformation (or data) in manufacturing information module 255. Themanufacturing data may be at any combination of substrate level, dielevel and packaged chip level. Once the manufacturing of thesemiconductor chips is completed, the products are shipped to storageand the product information (such as types and amounts) is recorded inthe inventory module 257. Afterwards, the products are delivered to thelocation(s) specified by chip provider A. Once the products aredelivered to specified location(s), the delivery information (such astypes and amount of chips and delivery dates) is recorded in theinventory module 205 of chip provider A and the inventory module 257 ofchip manufacturer B.

In the example of FIG. 2, the two information systems, system 200 andsystem 250, are not linked and often the information is not shared in atimely manner. Due to the lack of or delay of information sharing, ordermanagement, manufacturing planning, and material planning become verydifficult and inefficient, especially when the demand fluctuatessignificantly. Without proper planning, the fab utilization maysometimes be too high so that some customer orders might be turned down.On the other hand, the fab utilization may sometimes be too low, whichresults in waste of resources. Due to market fluctuation, chip providerA could request a pull-in (earlier delivery) or a pull-out (delayeddelivery) of orders. Such unexpected changes worsen the planning andmanagement of resources for chip manufacturer B.

To meet the demand of customers, chip manufacturers, such asmanufacturer B, normally keep a buffer inventory of already-madeproducts (semiconductor chips) in storage. As mentioned above, managinginventory level is important and challenging, since excess inventory iscostly and low inventory could result in stockout (or out of stock).Currently, there are two well-known methods of planning inventory. Onemethod is to plan inventory based on demand forecast. However, sincecustomers are fearful of stock-out, they tend to order extra when thedemand trends are up. Such buffer in ordering during periods of trendingup can come from multiple customers and results in significant overstockat the chip manufacturer's storage facilities. Such overstock phenomenonduring demand trending up periods is commonly called a bullwhip effect.

Another method of inventory planning is based on shipment. The methodcan also be called a demand-pull method. For this method, the inventoryis kept to a constant level. If more products are shipped, more productsare made to replenish the stock, and vice versa. Such inventory planningmethods were very popular in the 1980s and were popularized by ToyotaMotor. However, such methods work well in a stable market but do notwork well in a market with frequent and significant fluctuations. Whenthe demand fluctuates often and by significant amounts, the inventorycan easily run out. In addition, semiconductor chip manufacturing haslong lead-times. This method does not work well with products with longlead-times.

To avoid the problems associated with overstock and stock-out describedabove, it is desirable to have an integrated supply chain with a dynamicinventory control algorithm that can respond to the changes in order anddemand forecast effectively. If the demand forecast and inventoryinformation of customers (chip providers) are known to the chipmanufacturers (especially in real time), the chip manufacturers can moreeffectively plan and respond to fluctuations in the market. In addition,a dynamic inventory control method that can respond to fluctuation inshipment and forecast would help to minimize the impact of thefluctuation.

FIG. 3 shows production management system 300, in accordance with oneembodiment of this disclosure. The production management system 300 isused to manage the production of semiconductor chips produced by a chipmanufacturer, such as chip manufacturer B described above. Theproduction management system 300 includes a production planning module310 and a dynamic inventory control module 320. In the example of FIG.3, the production planning module 310 includes a demand forecastcomponent 311, which may store demand forecast data from customers, thesales data of the customers, the inventory data from the customers, theshipping histories of various products of the chip manufacturer, andmarket research data of semiconductor chips. The forecast component 311is configured to calculate and estimate the demand curve based on theinformation collected in the system. If the customers' sales data,demand forecast and inventory data are included, the forecast demandcomponent 311 can more accurately calculate and estimate the demandcurve. Further, if the customers' data were fed to the demand forecastin real time or with little delay, the demand prediction would be evenmore accurate. The shipping histories of products are useful inproviding seasonal demand trends in products. Market research data areuseful in predicting overall market trend and for predicting demands fornew products.

Module 310 also includes an order component 312, which stores orderinformation. Product order directly affects production, inventory andshipment. Further, module 310 includes a fab capacity component 313,which includes information related to the manufacturing capacity andtypes of products manufactured in the fab(s). Manufacturing fab(s) of achip manufacturer often needs to make different types of chips formultiple customers. A piece of manufacturing equipment can be used toprocess different products. The availability of production equipmentsaffects production schedule and planning. Module 310 may further includea product technology component 314. Different types of semiconductorchips use different photolithography masks, and may require differentnumbers of lithographical layers. Further, different products (types ofchips) may use different process flows, and are under different processtechnology nodes. For example, some chips utilize 65 nm technology,while others might use 40 nm technology. Different process technologynodes could use different processes and equipment in some process steps.Sometimes, substrate sizes can be different, such as 8 inches versus 12inches. In addition, module 310 may include a product priority component315. The semiconductor foundry fabrication facility receives orders fora variety of products. In one embodiment, some products are marked tohave different production priorities from others. Such priorityinformation is stored in the product priority component 315.

The production planning module 310 is coupled to (or connected to) thedynamic inventory control module 320. The dynamic inventory controlsystem 320 has an inventory management system 340, which storesinventory and shipment data. In the example shown in FIG. 3, theinventory management system 340 has a shipment information component 341an inventory information component 342. The data in components 341 and342 are real shipment and inventory data, not simulated data. Theinventory and shipping data may be current, historical and predictive(future targeted). The inventory control system 320 further includes adynamic inventory control simulation module 330, which performsimulations based on real time information, historical trends and marketresearch information. The simulation module 330 is capable ofdynamically simulating and predicting a number of parameters in responseto real time data and real time changes. The simulation module 330simulates the number of parameters for the next few days, next fewweeks, next few months, and next few years (until the end of productlife cycle).

In one embodiment, the dynamic inventory control simulation module 330includes an inventory target simulator 331, which simulates idealinventory target based on a number of parameters. The inventory ofsemiconductor chips is typically reviewed on a regular basis by themanufacturer, such as daily, every few days, weekly, every few weeks,monthly, etc. In one embodiment, target inventory at the next reviewperiod (T_(i+1)) is expressed as equation (1):

T _(i+1)=IT_(i) +ΔT _(i)   (1)

Where “i” is a particular review period, and i+1 is the next reviewperiod after review period “i”. IT is the initial target inventory. ITcan be a function of time or a constant. In one embodiment, IT can beset based on a number of parameters, such as initial order (IO),historical trend (HT), seasonal effect (SE), etc. These relationshipsare expressed as equation (2):

IT_(i) =f(IO_(i), HS_(i), SE_(i), . . . )   (2)

ΔT is determined by a number of parameters, such as target inventory(T), current inventory (I), future (or simulated) inventory (FI), andfuture (or simulated) shipment (FS). Future inventory (FI) can also becalled forecast demand (FCST).

ΔT _(i) =f(T _(i) , I _(i), FI_(i), FS_(i), . . . )   (3)

The dynamic inventory control simulation module 330 also includes afuture inventory (FI) simulator 332, which simulates future inventorybased on real inventory (I), wafer start (WS), and future shipment (FS,based on forecast). In one embodiment, FI can be expressed as equation(4).

FI_(i+1) =I _(i)+WS_(i)−FS_(i)   (4)

I_(i) is the real inventory (not simulated) of review period “i”. WS_(i)describes how working chips can be produced by a number of wafers beingstarted (or being added to the processing line) at review period “i”. Inone embodiment, WS_(i) is calculated by the number of wafers started perperiod (number of wafers being put into process line) multiplying thenumber of dies on a wafer (or substrate), and multiplying a fraction ofusable chips out of the number of dies on the wafer. WS is determined bya number of parameters, such as target inventory (T), real inventory(I), future inventory (FI), future shipment (FS), etc.

WS_(i) =f(T _(i) , I _(i), FI_(i), FS_(i), . . . )   (5)

Future shipment (FS) can be determined by a number of parameters, suchas historical trend (H), seasonal effect (SE), target inventory (T),current inventory (I), future inventory (FI), future shipment (FS),etc., as shown in equation (6) below.

FS_(i) =f(H _(i), SE_(i) , T _(i) , I _(i), FI_(i), FS_(i), . . . )  (6)

Simulation module 330 further includes a future shipment simulator 333,which simulates future shipment based on a number of parameters,described in equation (6) above. In addition, the simulation module 330includes a production simulator 334, which includes a wafer start (WS)simulator 335 and a cycle time (CT) simulator 336, in accordance withone embodiment of this disclosure. The relationship between wafer start(WS) and a number of parameters that affect WS has been shown above inequation (5). Cycle time describes how much time it takes to produce thechips. Since different types of chips require different processsequences and different lithographic masks, the cycle time of a productis often measured in numbers of days to finish a layer (days/layer).Each product has a cycle time, which is estimated by dividing the numberof days to finish the product by the number of lithographic layers. Forexample, if a product takes 90 days to complete and there are 60lithographical layers, the cycle time of this product is 1.5 days/layer.In one embodiment, cycle time (CT) can be expressed as equation (7).

CT_(i+1)=CT₀+ΔCT_(i)   (7)

Where CT₀ is the initial fab cycle time. CT₀ is affected by a number ofparameters, such as Product Technology (PT), Product Priority (PP), andFab capacity (FC).

CT₀ =f(PT, PP, FC)   (8)

ΔCT is affected by a number of parameters, such as target inventory (T),real inventory (I), future (or simulated) inventory (FI), future (orsimulated) shipment (FS), etc., as shown in equation (9) below.

ΔCT_(i+1) =f(T _(i) , I _(i), FI_(i), FS_(i), CT₀, . . . )   (9)

The various simulators in the dynamic inventory control simulationmodule 330 uses the information in module 310 and in the inventorymanagement system 340 to predict the ideal target inventory, futureinventory, future shipments, wafer starts, and cycle times to assist theproduction of semiconductor chips.

As mentioned above, the important task of the dynamic inventory controlsimulation module 330 is to anticipate and to respond to upcoming,sudden, or immediate changes (unexpected changes) in demand. If thereare changes in demand, there needs to be an algorithm to determine ifsuch changes are significant enough to warrant production change.Typically the target inventory includes a buffer inventory to preventstock becoming too low. FIG. 4 shows a diagram 400 of inventory andtarget inventory as a function of time, in accordance with oneembodiment of this disclosure. The Y-axis of the diagram 400 isinventory and the X-axis is time. In diagram 400, T_(end) corresponds tothe end time of a product cycle. There are 4 curves in FIG. 4. Curve 401represents the target inventory, which starts at T₀ (initial inventorytarget). The area under curve 401 is divided into three zones. Zone 1 isdefined by curve 402 and zero inventory (Y=0). Zone 1 is a region inwhich the inventory is considered to be low. If the inventory falls intothis low region, there is a great risk of stockout. Zone 2 is definedbetween curve 402 and 403 and is considered a safe zone. The inventoryis not too high and not too low. Zone 3 is defined between curve 403 andtarget inventory (401). Inventory in this region is relatively high. Aswe have discussed above, there is a cost associated with having arelatively high inventory. The ideal situation is to have the realinventory fall back into Zone 2.

Curve 405 of FIG. 4 is the real inventory. As we can see from FIG. 4,the real inventory of this product falls mostly in Zone 2, with anexception at t_(out). At t_(out), the inventory (curve 405) crossescurve 403, and the real inventory almost goes into Zone 3. In this case,there is no need to change the target inventory or production plan.However, another real inventory curve 405″ was in Zone 2 only beforet_(out). After t_(out), the inventory continues to go up, possibly dueto a reduction in market demand. At t_(ex), curve 405′ is even higher(exceeding) the target inventory. With the weak demand of the market andalso without proper correction, the inventory (cure 405′) continues tostay high, which results in a large amount of inventory (I_(ex)) at theend of product life cycle (at T_(end)).

When curve 405′ crosses curve 403 to move into Zone 3 for a period, theproduction plan and inventory target should be altered to avoid theexcess inventory situation we described above. Similarly, if theinventory falls too low to an extent that signals a risk of stock out,the production plan and inventory target should also be modified.Therefore, it is important to establish an algorithm that identifies aninventory control turning point. When an inventory control turning pointhas been reached, or meets the criteria to make production planningmodification, the simulators determine the types and amount of changesneeded. The simulators make the best and appropriate adjustment based onthe data in the system, instead of over-reacting as occurs without aproper calculation.

Algorithm I

Algorithm I is used to determine an inventory control turning point oftoo much inventory, in accordance with one embodiment of thisdisclosure.

If I_(i)>CU_(i)*T_(i) over M number of review cycles, i, i+1, i+2, . . .i+M−1

Lower future inventory target T_(i+M), T_(i+M+1), T_(i+M+2), . . . .

where CU_(i) is an upper control (or threshold) fraction (a number lessthan 1), and T_(i) is the current inventory target at current time (t).CU_(i)*T_(t) is an upper inventory threshold signaling high inventory.If the real inventory data are greater than the defined upperthreshold(s) of inventory over an extended period, such as M reviewcycles, then the target inventory is lowered to prevent high inventory.As mentioned above, wafer start (WS), cycle time (CT), future shipment(FS), and future inventory (FI) can all be affected by changes in targetinventory (T). M can be any integer and represents the number of reviewcycles that triggers the target inventory change (or signals reaching aninventory control turning point). The criteria for reaching an inventorycontrol turning point are established to be high inventory(ies) (overthreshold CU_(i)*T_(i)) over a number of (M) review cycles. Oncecriteria for the inventory control turning point are met, the inventorytarget for the next review cycle and future review cycles, such asT_(t+M), T_(t+M+1), T_(t+M+2), . . . , are lowered. CU (upper controlfraction) can be a constant or can vary with the review period.

In one embodiment, the T_(t+M), T_(t+M+1), and T_(t+M+2) are adjustedaccording to the equations below:

T _(t+M) =T _(t+M) −R _(u) *T _(t+M−1),

T _(t+M+1) =T _(t+M+1) −R _(u) *T _(t+M−1),

T _(t+M+2) =T _(t+M+2) −R _(u) *T _(t+M−1),

Where R_(u) is a reduction ratio (<1). R_(u)*T_(t+M−)1 represents theamount of target inventory to be reduced at T_(t+M). Alternatively, theT_(t+M), T_(t+M+1), and T_(t+M+2) are adjusted according to theequations below:

T _(t+M) =T _(t+M) −R _(u) *T _(t+M−1),

T _(t+M+1) =T _(t+M+1) −R _(u) *T _(t+M),

T _(t+M+2) =T _(t+M+2) −R _(u) *T _(t+M),

R_(u) can be a constant or can change based on a number of parameters,such as the value of inventory target and time (review period, time ofyear, . . . ), etc.

In one embodiment, the number of cycles (M) with high inventory(ies) aredefined based on a number of factors, such as the amount of highinventory, the requirement of business, the type of chip, historicaltrend, etc. M could be one, two, or more review cycles. Further, thetarget inventory are lowered for a number of review cycles (such as ncycles, where n is an integer) or for all future cycles.

EXAMPLE I

One example of applying algorithm is described below. In this example,the initial target inventory is set to be T₀, which is a constant. Thealgorithm for reaching inventory control turning point is shown below.

When I_(i)>(2/3)T_(i) for 3 review period (t, t+1, t+2),

-   -   Set T_(i+3)=T_(i+3)−(1/3)T_(i+2),

T _(i+4) =T _(i+4)−(1/3)T _(i+2),

T _(i+5) =T _(i+5)−(1/3)T _(i+2),

Once the inventory control turning point is reached, all future targetinventories are adjusted. In this example, the CU is 2/3 and R_(u) is1/3. As described above, once the target inventory is adjusted, othersimulated parameters are also adjusted. Adjusting the wafer start andcycle time will take a while to affect the inventory, since there is alead-time in wafer and chip production. However, other parameters, suchas future shipment and future inventory, can be adjusted immediately.

FIG. 5A shows a diagram of inventory and shipment of a product with andwithout using the algorithm described above in Example I to correct thetarget inventory, in accordance with one embodiment of this disclosure.The double-dotted curve 501 is the inventory curve without utilizing theinventory target correction algorithm. Without the correction, theinventory eventually goes out of control and exceeds the targetinventory. In contrast, the solid curve 502 employs the correctionalgorithm. In the example shown in FIG. 5A, the target inventory isadjusted after inventory exceeds the upper inventory threshold for threeconsecutive review periods, W05, W06, and W07. After three weeks of highinventory, the target inventory is adjusted to be lower for W07, W08,and beyond. In the example in FIG. 5A, the shipment is reducedsignificantly in W08 and beyond. The lowering of the target inventory ofW07 and beyond helps to bring the inventory back to the ideal zone ofoperation (middle zone between the upper inventory threshold TH_(u) andlower inventory threshold TH_(l)). Note that the shipment in FIG. 5A isat a different scale in comparison to the inventory.

FIG. 5B shows a process flow 510 of controlling product inventory of asemiconductor chip, in accordance with one embodiment of thisdisclosure. At operation 511, the initial forecast for target inventory,wafer start, shipment, inventory, cycle time, upper inventory thresholdand lower inventory threshold is established for a product (a particulartype of semiconductor chip). In one embodiment, the initial forecast isestablished for the entire product cycle. In another embodiment, theinitial forecast is established for a period (not an entire productcycle). As described above, the initial forecast could be based oncustomer orders, customer forecast, shipping forecast, inventoryforecast, current inventory, historical product data, and/or marketresearch information. At operation 512, the manufacturing of the productis started. Alternatively, operation 512 can occur before operation 511.At operation 513, the actual inventory and target inventory data of thecurrent period and the historical periods are reviewed and compared on aperiodic basis. At operation 514, a decision is made regarding if theactual (or real) inventory exceeds an upper inventory threshold for anumber of consecutive review periods. If the answer is yes, theoperation proceeds to operation 515. At operation 515, the forecast oftarget inventory is reduced (or lowered). Examples of how the forecastsof target inventory can be adjusted are shown above. At the nextoperation 516, the forecast of wafer starts, inventory, shipment, cycletime, upper inventory threshold and lower inventory threshold isadjusted based on the forecast of target inventory arrived (orcalculated) at operation 514. After operation 516 and at operation 517,it is decided if the end of product life cycle (or end of production)has been reached. If the answer is “yes”, the process flow is completed.If the answer is “no”, process flow returns to operation 513 to reviewand compare real inventory and target inventory data at the next reviewperiod. In addition, if the answer at operation 514 is “no”, theoperation proceeds to operation 517.

Algorithm II

Algorithm II is used to determine an inventory control turning pointcorresponding to too little inventory, in accordance with one embodimentof this disclosure.

If I_(i)<CL_(i)*T_(i) over O review periods, i, i+1, i+2, . . . i+O−1

Decrease future cycle time CT_(i+O), CT_(i+O+1), CT_(i+O+2), . . . , and

Increase future wafer start WS_(i+O), WS_(i+O+1), WS_(i+O+2), . . . .

where CL_(i) is a lower control (or threshold) fraction (a number lessthan 1), and T_(i) is the current inventory target at current time (t).CL_(i)*T_(t) is a lower inventory threshold signaling low inventory. Ifthe real inventory data are greater than the defined lower threshold(s)of inventory over a number of periods, such as O review periods, thenthe cycle time needs to be reduced and wafer start needs to be increasedto raise the production rate. As mentioned above in equation (4), waferstart (WS) and cycle time can affect future inventory (FI). O can be anyinteger and represents the number of review cycles that signal reachingan inventory control turning point. Since running out of stock (orstockout) is highly undesirable, O is a small integer number. In oneembodiment, O is smaller than M.

EXAMPLE II

One example is described below. The algorithm for determining aninventory control turning point is shown below.

When I_(i)<(1/3)T_(i) for one review period (i),

-   -   Set CT_(i+1)=CT_(i+1)−F_(CT)*CT_(product), and

WS_(i+1)=WS_(i+1) +B*T _(i)

where F_(CT) is a cycle time fraction (a less than 1 number that isrelated to cycle time) and CT product is the cycle time of the product.The cycle time of the product can be shortened by running hot lots(cassettes of substrates identified to have processing priority comparedto other lots). FCT is a number signaling how much a cycle time can beshortened as described in equation (10) below.

Processing time for regular lots/Processing time for hot lots=1+F _(CT)  (10)

By running hot lots, the cycle time can be shorted byF_(CT)*CT_(product). The inventory can also be increased by increasingwafer starts. For example, F_(CT) can be 0.2, 0.3, or other less than 1numbers. As shown above, the wafer start (or the number of wafers beingstarted in a particular period) can be increased by a buffer amount(B*Ti). Extra wafers are started to ensure sufficient inventory and toprevent stockout. B is a positive number that is less than 1.

FIG. 6A shows an example of an inventory of a product with and withoutthe utilization of the algorithm II described above to correct thetarget inventory. The dotted curve 601 is the inventory curve withoututilizing the inventory target correction algorithm. Without thecorrection, the inventory ran out of stock. In contrast, the solid curve602 employs the correction algorithm (algorithm II). In the exampleshown here, the inventory target is adjusted after the inventory fallsbelow the lower threshold of inventory control (TH_(l)) at W8. The cycletime and wafer start are adjusted immediately without delay. Theshipment is relatively higher in W8, W9 and W10. The decrease in cycletime and the increase in wafer start help to bring the inventoryeventually back to the safe middle zone of between Th_(u) (upperthreshold) and Th_(l) (lower threshold) of inventory. Note that theshipment in FIG. 6A is at a different scale in comparison to theinventory.

FIG. 6B shows a process flow 610 of managing product inventory usingalgorithm II, in accordance with one embodiment of this disclosure. Atoperation 611, the initial forecast for target inventory, wafer starts,shipment, inventory, cycle time, upper inventory threshold and lowerinventory threshold are established for a product (a particular type ofsemiconductor chip). At operation 612, the manufacturing of the productis started. Alternatively, operation 611 can occur after operation 612.At operation 613, the actual inventory and target inventory data of thecurrent period and the historical periods are reviewed and compared on aperiodic basis. At operation 614, a decision is made regarding if theactual (or real) inventory falls below a lower inventory threshold for anumber of consecutive review periods. If the answer is yes, theoperation proceeds to operation 615. At operation 615, the forecast ofcycle time is reduced and the forecast wafer start is increased. At thenext operation 616, the forecast of target inventory, inventory,shipment, upper inventory threshold and lower inventory threshold areadjusted based on the forecast of cycle time and wafer start times(orcalculated) at operation 615. After operation 616 and at operation 617,a decision of if the end of product life cycle (or end of production)has been reached or not is made. If the answer is “yes”, the processflow is completed. If the answer is “no”, process flow returns tooperation 613 to review and compare real inventory and target inventorydata at the next review period. In addition, if the answer at operation614 is “no”, the operation proceeds to operation 617.

Algorithm III

Algorithm III is used to determine an inventory control turning pointfor too little inventory.

If I_(j)<0 for any period in the future (j is a review period in thefuture)

Decrease CT_(j-leadtime)=CT_(j-leadtime)−F_(CT)*CT_(product), and

Increase WS_(j-leadtime)=WS_(j-leadtime)−I_(j)+B*T_(j) if possible,otherwise,

decrease CT and increase WS at the earliest possible cycle.

where CT_(j-leadtime) is the cycle time at period (j-leadtime) andWS_(i-leadtime) is the wafer start at period (j-leadtime). When theinventory forecast (simulated) is less than 0, the wafer start needs tobe increased and the cycle time needs to be shortened to prevent thisfrom happening or to keep the risk to a minimum. Typically, there is alead-time for semiconductor chip manufacturing. Depending on theproducts, the complexity of the manufacturing process, and the fabcapacity, the lead-time for a product can range from a few weeks to afew months. If based on the inventory simulation, the future inventoryof one or more periods are less than zero (stockout), the cycle timeneeds to be reduced and the wafer starts need to be increased possibly alead-time before the simulated stockout period. As shown above, thewafer starts can be increased by the amount of deficit in the inventory(−I_(i) is a positive value). In addition, a buffer amount (B*T_(i),where B is a fraction) can be added to ensure sufficient inventory. Theamount S_(i)−I_(i) is added to the wafer start because is likely thatthe situation is caused by a spike due to earlier shipping.

Since there is a lead-time for manufacturing, the cycle time and waferstart could be corrected a lead-time ahead of the time (i) that hasstockout problem. However, sometimes, when the time “i” is identified tohave a stockout problem, the time between now and time “i” is alreadyless than the lead-time for the product. When this happens, the cycletime and the wafer time need to be adjusted as early as possible. Oncethe cycle time and wafer start are adjusted, the simulation can be usedto see if the stockout at period “i” can be avoided or the amount ofstockout be minimized. To bring the stock back, hot lots (with shortcycle time) and increased wafer starts might need to be applied for anumber of periods. If the inventory is adjusted to be greater than 0,but less than the lower threshold of the target inventory, the algorithmdescribed in FIGS. 6A-6C should be used until the inventory is in the“safe” zone (zone 2).

EXAMPLE III

One example of algorithm III is described below. The algorithm forreaching inventory control turning point is shown below.

When I_(j)<0 for one future review period (j),

-   -   CT_(j-4 weeks)=CT_(j-4 weeks)−0.2*CT_(product), and        -   WS_(j-4 weeks)=WS_(j-4 weeks)−I_(j)+1/3*T_(j) if possible,            otherwise, decrease CT and increase WS at the earliest            possible cycle.            where F_(CT) is 0.2 and the lead-time is 4 weeks. B in this            example is 1/3.

FIG. 7A shows an example of an inventory level of a product with andwithout the utilization of the algorithm III described above to correctthe target inventory. The dotted curve 701 is the inventory curvewithout utilizing the inventory target correction algorithm. Without thecorrection, the inventory ran out of stock. In contrast, the solid curve702 employs the correction algorithm (algorithm II). In the exampleshown here, the inventory forecast indicates that future inventory willfall below zero at W8 due to a sudden change in shipment forecast. Thiswas found out after W3. The cycle time and wafer start are adjustedimmediately without delay at W4. The adjustment avoided stockout. Thedecrease in cycle time and increase in wafer starts help to bring theinventory eventually back to the safe middle zone of between Th_(u)(upper threshold) and Th_(l) (lower threshold) of inventory. Note thatthe shipment in FIG. 7A is at a different scale in comparison to theinventory.

FIG. 7B shows a process flow 710 of managing product inventory usingalgorithm III, in accordance with one embodiment of this disclosure. Atoperation 711, the initial forecast for target inventory, wafer starts,shipment, inventory, cycle time, upper inventory threshold and lowerinventory threshold are established for a product (a particular type ofsemiconductor chip). At operation 712, the manufacturing of the productis started. Alternatively, operation 711 can occur after operation 712.At operation 713, the forecast of inventory data is reviewed on aperiodic basis. At operation 714, a decision is made regarding if futureinventory of an upcoming period falls below zero. If the answer is yes,the operation proceeds to operation 715. At operation 715, the cycletime is reduced and the wafer starts are increased starting at a periodas early as possible and less than a lead-time from the identifiedperiod (the period the inventory forecast falls below zero). At nextoperation 716, the forecast of target inventory, inventory, shipment,upper inventory threshold and lower inventory threshold are adjustedbased on the forecast of cycle time and wafer start times(or calculated)at operation 715. After operation 716 and at operation 717, a decisionof if the end of product life cycle (or end of production) has beenreached or not is made. If the answer is “yes”, the process flow iscompleted. If the answer is “no”, process flow returns to operation 713to review and compare real inventory and target inventory data at thenext review period. In addition, if the answer at operation 714 is “no”,the operation proceeds to operation 717.

FIG. 8 shows a process flow 800 of managing product inventory usinginventory control algorithms (such as algorithms I, II, III mentionedabove), in accordance with one embodiment of this disclosure. Atoperation 801, the initial forecast for target inventory, wafer starts,shipment, inventory, cycle time, upper inventory threshold and lowerinventory threshold are established for a product (a particular type ofsemiconductor chip). At operation 802, the manufacturing of the productis started. Alternatively, operation 801 can occur after operation 802.At operation 803, the forecast of inventory data is reviewed on aperiodic basis. At operation 804, a decision is made regarding if aninventory control turning point has been reached. If the answer is yes,the operation proceeds to operation 805. At operation 805, productioncontrol parameters, such as target inventory, cycle time, wafer starts,etc., are modified to bring inventory level to a safe operating level.At next operation 806, the forecast of other production and inventorycontrol parameters, are adjusted based on the modifications at operation805. After operation 806 and at operation 807, a decision of if the endof product life cycle (or end of production) has been reached or not ismade. If the answer is “yes”, the process flow is completed. If theanswer is “no”, process flow returns to operation 803 to review andcompare real inventory and target inventory data at the next reviewperiod. In addition, if the answer at operation 804 is “no”, theoperation proceeds to operation 807.

Utilizing the methods and systems described above help to reduce thecost of overstock and the risk of stockout and result in substantialcost saving and good customer relationship. The embodiments of methodsand systems described above are merely examples. Other variations ofmethods and systems based on the same principles are also applicable. Inaddition, the methods and systems can be modified to be applied toinventory control of products that are not semiconductor chips.

Various modifications, changes, and variations apparent to those ofskill in the art may be made in the arrangement, operation, and detailsof the methods and systems disclosed. The embodiments may includevarious steps, which may be embodied in machine-executable instructionsto be executed by a general-purpose or special-purpose computer (orother electronic device). Such a general-purpose or special-purposecomputer 900 is illustrated in FIG. 9. The computer 900 comprises acentral processing unit (CPU) 904 that executes instructions stored inread only memory (ROM) 908, and storage device 910, using random accessmemory (RAM) 906 as working memory. The CPU 904 communicates with theseother devices over a bus 902. The computer 900 interfaces with a uservia input device 930, display device 932 and cursor control 934. Thecomputer 900 also comprises a communication interface 920 that enablescommunication with other computers such as Web server 952 and hostcomputer 942 via network 940 or the Internet 950. Alternatively, thesteps may be performed by hardware components that contain specificlogic for performing the steps, or by any combination of hardware,software, and/or firmware. Embodiments may also be provided as acomputer program product including a machine-readable medium havingstored thereon instructions that may be used to program a computer, suchas computer 900 (or other electronic device) to perform processesdescribed herein. The machine-readable medium may include, but is notlimited to, floppy diskettes, optical disks, CD-ROMs, DVD-ROMs, ROMs,RAMs, EPROMs, EEPROMs, magnetic or optical cards, or other type ofmedia/machine-readable medium suitable for storing electronicinstructions. Such machine-readable media may be read, for example, byan input device 930 attached to computer 900. For example, instructionsfor performing described processes may be transferred from a remotecomputer (e.g., a server) to a requesting computer (e.g., a client) byway of data signals embodied in a carrier wave or other propagationmedium via a communication link (e.g., network connection).

One aspect of this description relates to a production management systemfor dynamically controlling inventory of a semiconductor product toprevent overstock and stockout. The production management systemincludes a production planning module including components containingdata of demand forecast, and customer order. The production managementsystem further includes a dynamic inventory control module including adynamic inventory control simulation module and an inventory managementsystem, wherein the dynamic inventory control simulation module isconfigured to adjust a target inventory if a current inventory is beyonda threshold multiplied by the target inventory for M number of reviewcycles.

Another aspect of this description relates to a production managementsystem for dynamically controlling inventory to prevent overstock andstockout. The production management system includes a productionplanning module, wherein the production planning module is configured todetermine a demand curve for at least one product. The productionmanagement system further includes a dynamic inventory control moduleconnected to the product planning module. The dynamic inventory controlmodule includes a dynamic inventory control simulation module configuredto generate instructions for altering a production process based on thedemand curve. The dynamic inventory control module further includes aninventory management system configured to store real inventor data andreal shipping data.

Still another aspect of this description relates to a productionmanagement system for dynamically controlling inventory of asemiconductor product to prevent overstock and stockout. The productionmanagement system includes a production planning module comprising afirst processor configured to establish an initial forecast of targetinventory, wafer starts, inventory, shipment, cycle time, upperinventory threshold, and lower inventory threshold for a productincluding components containing data of demand forecast, and customerorder. The production management system further includes a dynamicinventory control module comprising a second processor configured to:review and compare real inventory and target inventory on a periodicbasis. The second processor is further configured to reduce the forecastof target inventory, responsive to a determination that the realinventory exceeds the upper inventory threshold for a number ofconsecutive review periods. The second processor is further configuredto repeat the step of reviewing and comparing real inventory and targetinventory data and the step of reducing the forecast of targetinventory, responsive to a determination that an end of product lifecycle has not been reached.

Although the foregoing disclosure has been described in some detail forpurposes of clarity of understanding, it will be apparent that certainchanges and modifications may be practiced within the scope of theappended claims. Accordingly, the present embodiments are to beconsidered as illustrative and not restrictive, and this disclosure isnot to be limited to the details given herein, but may be modifiedwithin the scope and equivalents of the appended claims.

What is claimed is:
 1. A production management system for dynamicallycontrolling inventory to prevent overstock and stockout, the productionmanagement system comprising: a production planning module includingcomponents containing data of demand forecast, and customer order; and adynamic inventory control module including a dynamic inventory controlsimulation module and an inventory management system, wherein thedynamic inventory control simulation module is configured to adjust atarget inventory if a current inventory is beyond a threshold multipliedby the target inventory for M number of review cycles, wherein M is aninteger.
 2. The production management system of claim 1, wherein thedynamic inventory control simulation module is configured to reduce thetarget inventory by a reduction ratio each future review cycle if thethreshold is an upper threshold.
 3. The production management system ofclaim 2, wherein the reduction ratio is constant over time.
 4. Theproduction management system of claim 1, wherein the dynamic inventorycontrol simulation module is configured to provide instructions toreduce a cycle time if the threshold is a lower threshold.
 5. Theproduction management system of claim 4, wherein the dynamic inventorycontrol simulation system is configured to provide instructions toreduce the cycle time by providing instructions to run hot lots.
 6. Theproduction management system of claim 1, wherein the dynamic inventorycontrol simulation system is configured to provide instructions toincrease a wafer start parameter if the threshold is a lower threshold,wherein the wafer start parameter is a number of usable chips obtainedper period.
 7. The production management system of claim 1, wherein M isgreater if the threshold is an upper threshold than if the threshold isa lower threshold.
 8. A production management system for dynamicallycontrolling inventory to prevent overstock and stockout, the productionmanagement system comprising: a production planning module, wherein theproduction planning module is configured to determine a demand curve forat least one product; and a dynamic inventory control module connectedto the product planning module, wherein the dynamic inventory controlmodule includes: a dynamic inventory control simulation moduleconfigured to generate instructions for altering a production processbased on the demand curve; and an inventory management system configuredto store real inventory data and real shipping data.
 9. The productionmanagement system of claim 8, wherein the production planning modulecomprises a fab capacity component configured to store informationrelated to a manufacturing capacity and types of products manufacturableby a fab.
 10. The production management system of claim 8, wherein theproduction planning module comprises a product technology componentconfigured to store information related to process flows, tools andmaterials usable for manufacturing products in a fab.
 11. The productionmanagement system of claim 8, wherein the production planning modulecomprises a product priority component configured to store informationrelated to a production priority of a product to be manufactured in afab.
 12. The production management system of claim 8, wherein thedynamic inventory control simulation module is configured to generateinstructions to alter a production priority of a product to bemanufacture in a fab.
 13. The production management system of claim 8,wherein the dynamic inventory control simulation module comprises atarget inventory simulator configured to simulate an inventory targetbased on an initial order parameter, a historical trend parameter, and aseasonal effect parameter.
 14. The production management system of claim8, wherein the dynamic inventory control simulation module comprises afuture inventor simulator configured to simulate a future inventoryparameter based on a real inventory parameter, a wafer start parameter,and a future shipment parameter.
 15. The production management system ofclaim 14, wherein the dynamic inventory control simulation modulefurther comprises a future shipment simulator configured to determinethe future shipment parameter based on a historical trend parameter, aseasonal effect parameter, a target inventory parameter, a currentinventory parameter, and a future inventory parameter.
 16. Theproduction management system of claim 14, wherein the dynamic inventorycontrol simulation module further comprises a wafer start simulatorconfigured to determine the wafer start parameter based on a targetinventory parameter, the real inventory parameter, the future inventoryparameter, and the future shipment parameter.
 17. The productionmanagement system of claim 14, wherein the inventory management systemis configured to store the real inventory parameter.
 18. A productionmanagement system for dynamically controlling inventory of asemiconductor product to prevent overstock and stockout, the productionmanagement system comprising: a production planning module comprising afirst processor configured to establish an initial forecast of targetinventory, wafer starts, inventory, shipment, cycle time, upperinventory threshold, and lower inventory threshold for a productincluding components containing data of demand forecast, and customerorder; and a dynamic inventory control module comprising a secondprocessor configured to: review and compare real inventory and targetinventory on a periodic basis; reduce the forecast of target inventory,responsive to a determination that the real inventory exceeds the upperinventory threshold for a number of consecutive review periods, andrepeat the step of reviewing and comparing real inventory and targetinventory data and the step of reducing the forecast of targetinventory, responsive to a determination that an end of product lifecycle has not been reached.
 19. The production management system ofclaim 18, wherein the second processor is further configured to reduce aforecast of the cycle time and increase a forecast of the wafer starts,responsive to a determination that the real inventory is below the lowerinventory threshold for a second number of consecutive review periods.20. The production management system of claim 18, wherein the secondprocessor is further configured to increase the forecast of targetinventory following a second number of cycles after the forecast oftarget inventory is reduced.